Hello everyone,
for the first time I am working with variables that are aggregated at the country-year level. I noticed that the point in my code where I winsorize the data (upper and lower 1%) has a huge influence on the descriptive statistics and the regression results.
Originally I learned to winsorize just before conducting the first (descriptive) analysis. As my final variables in this analysis will consist mainly of year- and country-level means, it seems wrong to me to include all the outliers in the means - and winsorizing the means in the end won't change anything, logically.
Do I winsorize just in the beginning, for all basic variables? Or right before building the mean of a variable? Also, I was wondering, if trimming the outliers completely would not be a better way when creating further variables.
These are three examples of how I construct the variables:
cid = country identifier, Year = time variable
In this first code, the variables of interest are BTD and BTC.
Second code: (Here, I left one possible point where I exemplary winsorized the data, but I am not sure if this is a statistical correct way to do it).
Here, the variables of interest are DACR_ABS and DACR_ABS_c, but I also continue to calculate with variable TACR_t
This is example 3, where I continue to work with variable TACR_t, and the variable of interest ist EM1.
Thank you in advance for your help!
This is the dataset. Next to the winsorized variables in the text, I also winsorized all variables in the beginning. Like this, the dataset best presents the relations should be in, but I highly doubt that is a good procedure for the final analysis.
(I could not find a way to attach it looking more readable, the reader's view always deforms it. I am happy to adapt it if there is a special command I have not found.)
for the first time I am working with variables that are aggregated at the country-year level. I noticed that the point in my code where I winsorize the data (upper and lower 1%) has a huge influence on the descriptive statistics and the regression results.
Originally I learned to winsorize just before conducting the first (descriptive) analysis. As my final variables in this analysis will consist mainly of year- and country-level means, it seems wrong to me to include all the outliers in the means - and winsorizing the means in the end won't change anything, logically.
Do I winsorize just in the beginning, for all basic variables? Or right before building the mean of a variable? Also, I was wondering, if trimming the outliers completely would not be a better way when creating further variables.
These are three examples of how I construct the variables:
cid = country identifier, Year = time variable
In this first code, the variables of interest are BTD and BTC.
Code:
gen PTBI = PLBT/l.TOAS generate Taxation = TAXA/l.TOAS gen temp = PTBI - (Taxation/STAX) gen PermBTD = abs(temp) drop temp bys cid Year : egen BTD= mean(PermBTD) (... Ranking) Scaling: tabstat BTD_rank, by(BTD) egen rank = max(BTD_rank) gen BTD_scale = BTD_rank/rank *Average rank over a three year period bys cid : egen BTC = mean(BTD_scale)
Here, the variables of interest are DACR_ABS and DACR_ABS_c, but I also continue to calculate with variable TACR_t
Code:
sort fid Year
gen TACR_t = (d.CUAS-d.CASH)-(d.CULI-d.LOAN)-DEPR
gen TACR = ((d.CUAS-d.CASH)-(d.CULI-d.LOAN)-DEPR)/l.TOAS
gen PPE = (TFAS+OFAS)/l.TOAS
gen pseudo_intercept = 1/l.TOAS
gen d_REV_REC = (d.OPRE-d.DEBT)/l.TOAS
gen DACR = .
sum ff48
forvalues X = `r(min)'/`r(max)' {
capture {
reg TACR pseudo_intercept d_REV_REC PPE ROA if ff48==`X'
predict res, res
replace DACR = res if ff48==`X' & `e(N)'>=100
drop res
}
}
gen DACR_ABS = abs(DACR)
winsor2 DACR_ABS, cuts(1, 99)
sum DACR DACR_ABS
bys cid Year ff48: egen DACR_ABS_c = median(DACR_ABS)
Code:
sort fid Year gen CFO = (PLAT - TACR_t) winsor2 CFO OPPL, cuts(1,99) gen CFO_scale = CFO/l.TOAS gen OPPL_scale = OPPL/l.TOAS bys fid : egen OPPL_sd = sd(OPPL_scale) bys fid : egen CFO_sd = sd(CFO_scale) bys fid: gen temp = OPPL_sd/CFO_sd bys cid Year: egen EM1 = median(temp)
This is the dataset. Next to the winsorized variables in the text, I also winsorized all variables in the beginning. Like this, the dataset best presents the relations should be in, but I highly doubt that is a good procedure for the final analysis.
(I could not find a way to attach it looking more readable, the reader's view always deforms it. I am happy to adapt it if there is a special command I have not found.)
Code:
fid cid Year PLBT TOAS PTBI Taxation PermBTD BTD BTC TACR TACR_t DACR DACR_ABS DACR_ABS_c CFO CFO_scale OPPL_scale EM1 2817 4 2017 4745.08385 58299.8695 0.0824614 0.02482222 0.00086259 6.588336 0.4074074 -0.00088067 -50.67625 0.04009051 0.04009051 0.10928206 3367.413 0.05851985 0.0810942 0.4120621 2817 4 2018 2253.9957 84180.0186 0.03866211 0.01319829 0.00533219 0.05211727 0.4074074 -0.09503116 -5540.305 -0.01275468 0.01275468 0.03686274 7024.842 0.12049498 0.02562361 0.4120621 2817 4 2019 2727.70514 89486.4554 0.03240324 0.01035516 0.00211395 0.05530543 0.4074074 -0.04133679 -3479.732 0.01791139 0.01791139 0.05070473 5335.74 0.06338488 0.02341303 0.4120621 18086 11 2017 16.961 27413.101 0.00067381 -0.00022978 0.00163122 1.5529467 0.345679 -0.06771223 -1704.45 -0.0425133 0.0425133 0.03222278 1727.195 0.06861581 0.00066975 0.3348516 18086 11 2018 40.109 29163.157 0.00146313 0.00060219 0.00104601 0.04311743 0.345679 -0.00983296 -269.552 0.02834461 0.02834461 0.02694934 293.153 0.0106939 0.0030164 0.3348516 18086 11 2019 71.269 31958.754 0.0024438 0.00094057 0.00147524 0.10434106 0.345679 -0.03144594 -917.063 0.00040662 0.00040662 0.05091326 960.902 0.03294918 0.00423219 0.3348516 12461 26 2017 2568.62122 20326.4696 0.11789087 0.02313979 0.0038975 2566.7444 0.04938272 -0.03096913 -674.7593 -0.01320734 0.01320734 0.06305698 2739.208 0.1257202 0.11487105 0.352464 18141 11 2017 664.516 26967.073 0.0312878 0.00102609 0.02701242 1.5529467 0.345679 -0.04669478 -991.742 -0.00490222 0.00490222 0.03222278 1634.465 0.0769565 0.04399341 0.3348516 12842 26 2017 484.04419 28529.2725 0.01753328 0.00106208 0.01194338 2566.7444 0.04938272 0.02884954 796.4542 0.05522585 0.05522585 0.06305698 -341.7311 -0.01237835 0.02316142 0.352464 12461 26 2018 2079.37905 23069.4666 0.10229908 0.02313239 0.01945036 4039.437 0.04938272 -0.06566333 -1334.7036 -0.02738268 0.02738268 0.06723364 2943.883 0.14483002 0.10622934 0.352464 14735 26 2017 5457.38167 67510.9793 0.0867826 0.01754733 0.00557178 2566.7444 0.04938272 0.02153486 1354.2338 0.04129442 0.04129442 0.06305698 2999.672 0.04770041 0.11926562 0.352464 14735 26 2019 3117.17516 55688.2856 0.04376696 0.0088075 0.00258829 316.06085 0.04938272 -0.23890907 -17015.607 -0.2184093 0.2184093 0.06154155 19505.494 0.27386853 0.06764886 0.352464 18141 11 2019 626.186 30695.16 0.0217053 0.0007554 0.01855778 0.10434106 0.345679 -0.10916464 -3149.34 -0.06459392 0.06459392 0.05091326 3753.733 0.13011454 0.0289237 0.3348516 18141 11 2018 430.108 28849.452 0.01594938 0.00080813 0.01258215 0.04311743 0.345679 -0.05097769 -1374.719 -0.00362433 0.00362433 0.02694934 1783.034 0.06611893 0.02434246 0.3348516 12842 26 2018 970.202841 29755.2765 0.03400728 -0.00915202 0.0821758 4039.437 0.04938272 -0.05048295 -1440.242 -0.00641199 0.00641199 0.06723364 2671.545 0.09364225 0.04103162 0.352464 14735 26 2018 5227.51659 71222.107 0.0774321 0.01587822 0.00613747 4039.437 0.04938272 0.05890192 3976.5264 0.07906541 0.07906541 0.06723364 179.0363 0.00265196 0.11028625 0.352464 12461 26 2019 -2299.15134 28575.4999 -0.0996621 -0.02270334 0.01982916 316.06085 0.04938272 0.01988966 458.8439 0.09009517 0.09009517 0.06154155 -2234.2415 -0.09684842 -0.13086022 0.352464 12842 26 2019 2503.68624 31723.6477 0.0841426 0.0156359 0.00184841 316.06085 0.04938272 -0.1142029 -3398.139 -0.08337249 0.08337249 0.06154155 5436.575 0.1827096 0.09041428 0.352464 13900 26 2018 1236.40673 92946.9044 0.01309846 0.00023881 0.01184159 4039.437 0.04938272 -0.04314405 -4072.5085 -0.01761709 0.01761709 0.06723364 5286.374 0.0560037 0.01891337 0.352464 13900 26 2019 188.694897 83797.0865 0.00203014 -0.00112785 0.00796621 316.06085 0.04938272 -0.09391215 -8728.844 -0.02832236 0.02832236 0.06154155 9022.369 0.09707014 -0.00060548 0.352464 8013 23 2017 1573.127 36502.566 0.05244766 0.00929099 0.01528371 0.067271 0.4074074 -0.2179192 -6536.318 -0.1386121 0.1386121 0.04456513 7830.769 0.26107588 0.06902349 0.3552493 13900 26 2017 1587.62673 94393.2799 0.01705955 0.00158864 0.00869829 2566.7444 0.04938272 0.03076962 2863.537 0.06350838 0.06350838 0.06305698 -1423.755 -0.0152987 0.02263202 0.352464 8013 23 2018 4466.796 39863.337 0.12236937 0.02506813 0.02209686 0.1677606 0.4074074 -0.12258103 -4474.522 -0.12470427 0.12470427 0.05864691 8026.267 0.21988226 0.11992294 0.3552493 8013 23 2019 3622.78 40713.513 0.09088 0.01692011 0.02319957 0.04799062 0.4074074 0.00227954 90.87 0.02979955 0.02979955 0.03129168 2857.418 0.07168035 0.1044076 0.3552493 11902 26 2018 25819.0465 172364.007 0.1548661 0.03451859 0.02681069 4039.437 0.04938272 0.0924698 15416.427 0.0939441 0.0939441 0.06723364 4647.7314 0.02787772 0.11226112 0.352464 11902 26 2017 24302.9497 166718.504 0.1631501 0.03328214 0.01201905 2566.7444 0.04938272 0.075545 11253.236 0.07336272 0.07336272 0.06305698 8091.983 0.05432295 0.12743378 0.352464 11902 26 2019 19740.463 185782.903 0.11452775 0.0255132 0.01975226 316.06085 0.04938272 0.01817813 3133.2556 0.03322161 0.03322161 0.06154155 12209.65 0.07083643 0.07340663 0.352464 11150 8 2017 10238 166354 0.0655807 -0.01958838 0.1243517 17139.715 0.1234568 -0.09712195 -15162 -0.08349518 0.08349518 0.0546836 28458 0.18229103 0.05791318 0.316187 11150 8 2018 16669 184658 0.10020198 -0.01543696 0.14698064 54.27909 0.1234568 -0.1066581 -17743 -0.09947996 0.09947996 0.04412192 36980 0.22229703 0.08493935 0.316187 11150 8 2019 9385 189738 0.05082368 -0.0223765 0.11863127 0.07839143 0.1234568 -0.21499205 -39700 -0.19587246 0.19587246 0.07554021 53217 0.2881922 0.04996263 0.316187 10568 8 2018 22686 254017 0.09363855 -0.00737188 0.11597759 54.27909 0.1234568 -0.10753203 -26052 -0.09525875 0.09525875 0.04412192 50524 0.20854247 0.09363855 0.316187 10568 8 2017 39724 242272 0.16799957 0.01399855 0.1259997 17139.715 0.1234568 -0.06048137 -14301 -0.06397611 0.06397611 0.0546836 50715 0.21448237 0.1534343 0.316187 10568 8 2019 32951.921 264412.112 0.1297233 -0.00303546 0.13892165 0.07839143 0.1234568 -0.02492523 -6331.432 -0.02063536 0.02063536 0.07554021 40054.41 0.15768397 0.12574823 0.316187 7839 23 2018 1066.55 23770.879 0.04716855 0.00537687 0.02566106 0.1677606 0.4074074 -0.04857634 -1098.3821 -0.01868373 0.01868373 0.05864691 2043.353 0.090368 0.06172878 0.3552493 7839 23 2017 323.52732 22611.4635 0.01658735 0.00573223 0.00634157 0.067271 0.4074074 0.08525405 1662.8342 0.1299022 0.1299022 0.04456513 -1451.111 -0.07439893 0.06080423 0.3552493 14775 26 2019 2853.7693 28115.5208 0.12262114 0.02639048 0.01627614 316.06085 0.04938272 0.1648402 3836.336 0.1901969 0.1901969 0.06154155 -1596.754 -0.06860954 0.12872753 0.352464 14775 26 2018 3337.32038 23273.0605 0.15273593 0.02755729 0.00769756 4039.437 0.04938272 0.0653158 1427.1674 0.05709546 0.05709546 0.06723364 1308.019 0.05986284 0.16197 0.352464 14775 26 2017 3033.52764 21850.2638 0.16619253 0.0341322 0.01345061 2566.7444 0.04938272 0.1577564 2879.5425 0.15837006 0.15837006 0.06305698 -469.033 -0.02569608 0.17896764 0.352464 7839 23 2019 -816.47 28662.921 -0.03434749 -0.00745576 0.00452444 0.04799062 0.4074074 -0.1785863 -4245.153 -0.11563886 0.11563886 0.03129168 3605.913 0.15169455 -0.03064561 0.3552493 22006 19 2017 1151.99686 114638.249 0.00709595 0.00082249 0.00195539 0.04390508 0.6419753 -0.00936604 -1520.5367 0.02837502 0.02837502 0.04112589 2539.006 0.0156395 0.02977014 0.4174105 22006 19 2019 9529.25346 109923.574 0.06893009 0.00342104 0.04754856 0.03877004 0.6419753 0.08016077 11081.84 0.10077485 0.10077485 0.02452792 -2025.5293 -0.01465172 0.02262637 0.4174105 22006 19 2018 1895.08882 138245.186 0.01653103 0.00186648 0.00486554 0.06418661 0.6419753 -0.07036359 -8066.359 -0.05762959 0.05762959 0.05451224 9747.478 0.08502814 0.02967739 0.4174105 21860 19 2017 17884.8777 101952.894 0.1761151 0.02144755 0.04206789 0.04390508 0.6419753 0.00658196 668.4124 -0.00370075 0.00370075 0.04112589 15038.42 0.14808556 0.19318733 0.4174105 15868 12 2017 405.159369 61481.5362 0.00679402 0.00216759 0.00524815 0.02964047 0.7530864 -0.1118641 -6670.981 -0.06356306 0.06356306 0.06356306 6946.877 0.11649054 0.02669115 0.3491954 15868 12 2018 829.631453 67128.6433 0.01349399 0.00356983 0.00633842 0.03924666 0.7530864 -0.08523504 -5240.381 -0.03262254 0.03262254 0.14398985 5850.534 0.0951592 0.02983445 0.3491954 15868 12 2019 1793.03987 72803.4824 0.0267105 0.00534231 0.00296901 0.02611423 0.7530864 -0.03665513 -2460.609 0.01686677 0.01686677 0.05952562 3895.0266 0.05802332 0.04456156 0.3491954 21860 19 2018 20213.8193 124859.174 0.19826627 0.02942579 0.01435509 0.06418661 0.6419753 -0.07651548 -7800.975 -0.08737852 0.08737852 0.05451224 25014.75 0.24535596 0.20482655 0.4174105 21860 19 2019 19188.4224 156810.502 0.1536805 0.0004183 0.15106615 0.03877004 0.6419753 0.0177234 2212.9292 0.01063253 0.01063253 0.02452792 16923.266 0.13553883 0.16577235 0.4174105 15352 26 2017 8898.01596 148165.823 0.05685988 0.02457833 0.07249975 2566.7444 0.04938272 -0.05788236 -9058.024 -0.01704199 0.01704199 0.06305698 14109.772 0.09016392 0.05547042 0.352464 15352 26 2019 -12908.9227 137719.703 -0.08500778 -0.03222789 0.08461267 316.06085 0.04938272 -0.04811766 -7306.945 0.01679573 0.01679573 0.06154155 -707.9871 -0.00466223 -0.08096199 0.352464 15352 26 2018 9480.03726 151855.783 0.06398262 0.01373194 0.00829076 4039.437 0.04938272 -0.0199172 -2951.0476 0.01835664 0.01835664 0.06723364 10396.48 0.07016787 0.06365335 0.352464 17636 11 2017 20788.893 199735.28 0.12279417 0.03162026 0.0089569 1.5529467 0.345679 0.10901812 18456.627 0.13301103 0.13301103 0.03222278 -3021.002 -0.01784421 0.132197 0.3348516 17636 11 2018 17602.427 240609.166 0.08812878 0.02222998 0.00449613 0.04311743 0.345679 -0.01779262 -3553.814 0.02097514 0.02097514 0.02694934 16716.13 0.08369143 0.1039849 0.3348516 17636 11 2019 19052.031 298984.014 0.07918248 0.00422377 0.06158346 0.10434106 0.345679 -0.04982003 -11987.156 -0.0149379 0.0149379 0.05091326 30022.91 0.12477875 0.08621065 0.3348516 11532 8 2019 7248 424925 0.01762235 0.00059082 0.015832 0.07839143 0.1234568 0.19893864 81822.63 0.24898374 0.24898374 0.07554021 -74817.63 -0.1819071 0.00343305 0.316187 11532 8 2018 8195.561 411295.828 0.02114044 0.00029804 0.02023728 54.27909 0.1234568 0.0669193 25942.75 0.11734951 0.11734951 0.04412192 -17862.734 -0.0460769 0.00767275 0.316187 11532 8 2017 7780.86 387672.198 0.02074138 0.00067225 0.01872444 17139.715 0.1234568 -0.1572441 -58988.07 -0.10642534 0.10642534 0.0546836 66516.75 0.17731322 0.00817811 0.316187 17663 11 2017 6101.546 211228.124 0.03217998 -0.00070175 0.03510394 1.5529467 0.345679 -0.06836262 -12962.026 -0.02247254 0.02247254 0.03222278 19196.629 0.10124435 0.00412878 0.3348516 17663 11 2018 12934.52 211275.583 0.06123484 0.00340757 0.04703664 0.04311743 0.345679 -0.01143031 -2414.402 0.02694934 0.02694934 0.02694934 14629.148 0.06925758 0.01549151 0.3348516 17663 11 2019 7828.127 218236.25 0.03705173 0.00193938 0.02897098 0.10434106 0.345679 -0.0342764 -7241.766 0.01046947 0.01046947 0.05091326 14660.15 0.06938876 0.01033848 0.3348516 8278 23 2019 7261.404 208579.005 0.0313216 0.00722673 0.00241466 0.04799062 0.4074074 -0.05143012 -11923.238 -0.01453135 0.01453135 0.03129168 17509.242 0.07552499 0.03894991 0.3552493 8278 23 2018 13004.245 231833.76 0.05400398 0.01309377 0.0016289 0.1677606 0.4074074 0.03385944 8153.408 0.06116798 0.06116798 0.05864691 1697.8358 0.00705077 0.06421751 0.3552493 8278 23 2017 16108.12 240801.611 0.1705609 0.03849348 0.01658697 0.067271 0.4074074 -0.2437021 -23015.72 -0.2324757 0.2324757 0.04456513 35488.44 0.3757696 0.1901406 0.3552493 8032 23 2017 3304.38802 60685.9224 0.05850715 0.01465877 0.00012795 0.067271 0.4074074 0.01695952 957.8459 0.04612201 0.04612201 0.04456513 1518.6385 0.02688886 0.0694515 0.3552493 8032 23 2019 3960.757 70241.9783 0.05780673 0.0144351 6.6321E-05 0.04799062 0.4074074 -0.05731983 -3927.396 -0.03154258 0.03154258 0.03129168 6899.1 0.10069146 0.06625017 0.3552493 8032 23 2018 3567.17534 68517.2258 0.05878093 0.01488304 0.00075123 0.1677606 0.4074074 0.01611146 977.7385 0.0437501 0.0437501 0.05864691 1686.2457 0.02778644 0.06972042 0.3552493 7893 23 2018 543.53 42135.957 0.01235401 0.00143683 0.0066067 0.1677606 0.4074074 -0.159011 -6995.887 -0.11456939 0.11456939 0.05864691 7476.202 0.1699282 0.01846981 0.3552493 7893 23 2019 1233.669 42435.724 0.0292783 0.00507234 0.00898893 0.04799062 0.4074074 -0.12777655 -5383.987 -0.1148497 0.1148497 0.03129168 6403.928 0.1519825 0.02939646 0.3552493 7893 23 2017 11.242 43996.242 0.00022848 0.00226717 0.0088402 0.067271 0.4074074 -0.07958093 -3915.5986 -0.01635631 0.01635631 0.04456513 3815.2896 0.07754225 0.00606692 0.3552493 20198 17 2018 7301.43979 44964.4881 0.1512755 0.02992853 0.00624304 0.03684286 0.5432099 -0.08132406 -3925.1736 -0.0632997 0.0632997 0.0557246 9782.088 0.20267105 0.1510382 0.3894069 20198 17 2017 5971.84558 48265.8377 0.1455135 0.02691003 0.00388176 21.34573 0.5432099 -0.13546728 -5559.551 -0.1075591 0.1075591 0.05372737 10427.014 0.25407076 0.14603199 0.3894069 20198 17 2019 6458.71128 50239.4159 0.14364026 0.02857417 0.00675008 0.0394582 0.5432099 -0.0754589 -3392.971 -0.05265442 0.05265442 0.04746133 8566.859 0.190525 0.14515823 0.3894069 12403 26 2018 1831.69843 21084.4899 0.08434094 0.01878781 0.01454229 4039.437 0.04938272 . . . . 0.06723364 . . 0.10011658 0.352464 12403 26 2017 1607.68154 21717.7834 0.07482594 0.01656972 0.01238314 2566.7444 0.04938272 . . . . 0.06305698 . . 0.08987502 0.352464 14014 26 2017 1244.91107 40066.984 0.03094948 0.00028009 0.02947534 2566.7444 0.04938272 . . . . 0.06305698 . . 0.07074968 0.352464 8035 23 2019 5568.347 71455.611 0.08320484 0.02367002 0.01147524 0.04799062 0.4074074 0.01413187 945.752 0.03291081 0.03291081 0.03129168 3038.518 0.04540296 0.08470124 0.3552493 12403 26 2019 1500.9017 26204.4589 0.07118511 0.01600973 0.0130766 316.06085 0.04938272 . . . . 0.06154155 . . 0.0896877 0.352464 8035 23 2017 -273.036 61265.652 -0.00523697 0.00071536 0.00809839 0.067271 0.4074074 -0.21616824 -11270.207 -0.2061729 0.2061729 0.04456513 10959.875 0.21021593 -0.00509223 0.3552493 8035 23 2018 5358.366 66923.356 0.08746117 0.02337178 0.00602593 0.1677606 0.4074074 0.07281438 4461.02 0.1318822 0.1318822 0.05864691 -534.541 -0.00872497 0.09060641 0.3552493 14014 26 2018 -732.901126 52805.4707 -0.0182919 -0.00110692 0.01246599 4039.437 0.04938272 . . . . 0.06723364 . . 0.02692589 0.352464 14014 26 2019 905.213088 54720.4232 0.01714241 . . 316.06085 0.04938272 0.03883414 2050.655 . . 0.06154155 . . 0.05764274 0.352464 12344 26 2017 1558.11042 33744.413 0.05687201 0.00583935 0.02613857 2566.7444 0.04938272 0.11494425 3149.1035 0.14114691 0.14114691 0.06305698 -1750.9727 -0.0639116 0.05189622 0.352464 12344 26 2018 2319.55999 34539.4901 0.06873908 0.01508186 0.0106391 4039.437 0.04938272 -0.10589373 -3573.322 -0.07338718 0.07338718 0.06723364 5383.954 0.15955096 0.07297777 0.352464 10458 8 2019 -7867.585 137525.796 -0.05910061 -0.02460579 0.0154624 0.07839143 0.1234568 0.03212542 4276.597 0.08705228 0.08705228 0.07554021 -8868.612 -0.06662024 -0.03256869 0.316187 10458 8 2018 971.501 133121.895 0.00753625 -0.01997768 0.06807468 54.27909 0.1234568 0.0194716 2510.093 0.04790367 0.04790367 0.04412192 1036.74 0.00804233 0.01482398 0.316187 10458 8 2017 -2951.909 128910.446 -0.02373426 -0.02512494 0.0516481 17139.715 0.1234568 -0.0283303 -3523.535 0.00732802 0.00732802 0.0546836 3696.499 0.02972099 -0.00588877 0.316187 3768 4 2017 2332.49638 44696.1628 0.0614609 0.0259248 0.02556429 6.588336 0.4074074 -0.09731121 -3693.048 -0.05046789 0.05046789 0.10928206 5041.675 0.13284731 0.0663941 0.4120621 12344 26 2019 4867.12637 46453.1997 0.14091483 0.03006318 0.01731245 316.06085 0.04938272 -0.02570677 -887.8986 0.01008846 0.01008846 0.06154155 4716.658 0.13655841 0.1474753 0.352464 3768 4 2018 4895.46861 56127.273 0.10952771 0.0217452 0.03704372 0.05211727 0.4074074 -0.00800858 -357.9529 0.02108388 0.02108388 0.03686274 4281.4946 0.0957911 0.0657604 0.4120621 3768 4 2019 1402.30024 62611.8765 0.02498429 0.0052699 0.00741797 0.05530543 0.4074074 -0.00050883 -28.55952 0.05070473 0.05070473 0.05070473 1135.0748 0.02022323 0.02352101 0.4120621 2296 24 2017 974.924424 27764.0022 0.03976626 0.00790245 0.0018256 0.1740682 0.7037037 -0.04352385 -1067.047 0.00173573 0.00173573 0.02452792 1848.232 0.07538765 0.04796743 0.4077041 2296 24 2018 914.025062 28241.0108 0.03292123 0.00387152 0.0125448 0.02991877 0.7037037 -0.0698714 -1939.9098 -0.03288082 0.03288082 0.13556993 2746.446 0.09892111 0.04137136 0.4077041 2296 24 2019 889.958285 30771.0082 0.03151298 0.00495491 0.00543453 0.02611423 0.7037037 -0.0601538 -1698.804 -0.02766701 0.02766701 0.06405019 2448.831 0.08671187 0.04287749 0.4077041 2037 24 2017 -3119.57017 52444.5426 -0.06046965 . . 0.1740682 0.7037037 -0.05948853 -3068.955 0.0133148 0.0133148 0.02452792 -50.6151 -0.00098112 -0.05378319 0.4077041 2037 24 2018 535.423068 55384.313 0.01020932 . . 0.02991877 0.7037037 0.09437573 4949.492 0.13556993 0.13556993 0.13556993 -4414.069 -0.0841664 0.00792254 0.4077041 2037 24 2019 1071.6017 57849.8857 0.01934847 9.95E-06 0.01929612 0.02611423 0.7037037 -0.07419669 -4109.3325 -0.03215412 0.03215412 0.06405019 5180.383 0.0935352 0.01742513 0.4077041 17238 11 2017 1280.977 132271.938 0.01118279 0.00010554 0.01074306 1.5529467 0.345679 0.08053026 9224.658 0.1405606 0.1405606 0.03222278 -7955.77 -0.06945301 0.01587699 0.3348516 17238 11 2018 -1960.754 135983.841 -0.01482366 0.00021881 0.01573535 0.04311743 0.345679 -0.01130778 -1495.702 0.03083419 0.03083419 0.02694934 -493.994 -0.00373469 -0.01064285 0.3348516 17238 11 2019 -14123.673 124176.531 -0.10386287 5.07E-06 0.103884 0.10434106 0.345679 -0.1843589 -25069.83 -0.11763403 0.11763403 0.05091326 10945.468 0.08049095 -0.0991344 0.3348516 21123 17 2018 -2338.985 154811.584 -0.0136963 . . 0.03684286 0.5432099 -0.1258326 -21489.05 -0.07578793 0.07578793 0.0557246 19150.068 0.11213631 -0.00508051 0.3894069
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